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ARPN Journal of Engineering and Applied Sciences                      November 2022  |  Vol. 17  No. 22
   
Title: Provision of Carboxymethyl Cellulose material based on durian seed powder
Author (s):

M. H. S. Ginting, A. Utama, H. Muhammad, Maulida R. Tambun and A. H. Rajagukguk

Abstract:

Carboxymethyl Cellulose (CMC) is a water-soluble derivative compound. Synthesis of this CMC includes the stages of cellulose alkalinization and carboxymethylation reactions. Durian seed flour is reacted with sodium hydroxide in isopropanol and sodium chloroacetate as a solvent. This study aims to determine the effect of temperature, carboxymethylation reaction time, volume variation of sodium hydroxide, and weight of sodium chloroacetate on the degree of substitution of resulting CMC. The results are shown that the greater the volume of 20%NaOH solution added and the longer carboxymethylation reaction time, the higher the carboxymethylcellulose Substitution Degree (DS) was produced. The most significant degree of substitution was the addition of 10 ml of 20%NaO Hand the reaction time lasted for 2 hours, namely 0.61.

   

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Title: Classification of geopolymer concrete grade with Convolutional Neural Network using LeNet architecture
Author (s):

Agustinus Agus Setiawan, Roesdiman Soegiarso, Harianto Hardjasaputra and Lina

Abstract:

Geopolymer concrete is one of the innovations in the field of construction materials, this kind of material can reduce the impact of carbon emissions on the environment. Geopolymer concrete is an environmentally friendly material, which does not use cement as a base material. Compressive strength is a quality parameter of geopolymer concrete as well as normal concrete. This study aims to model the compressive strength classification of geopolymer concrete using an artificial neural network. The classification process is based on the composition of the geopolymer concrete mixture by considering the geopolymer concrete curing process, including the temperature and duration of geopolymer concrete curing. Eight independent variables and one dependent variable were used in this modelling process. The artificial neural network model developed is a Deep Learning model, using the Convolutional Neural Network algorithm and LeNet network architecture. Three variations of hyper parameters were compared in this study, including variations in the number of epochs, learning rate values, ??and variations in the optimizer function. From the modelling results that have been made, the LeNet architectural model with 1000 epochs, a learning rate value of 0.001, and using the Adam optimizer function is able to produce the best model with a training accuracy rate of 86.15%, and an R-square value of 0.93. This model is able to produce a testing accuracy value of 79.80%. As an alternative, the RMSprop optimizer function is also able to produce an adequate model to classify the compressive strength of geopolymer concrete.

   

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Title: Preliminary evaluation of commercial additives using four-ball tribotester machine
Author (s):

Bukhari Manshoor, Mohd. Zaki Bahrom, Norfazillah Talib and Zulkifli Mohamed

Abstract:

This study evaluates the kinematic viscosity, coefficient of friction, and wear scar diameter of the commercial additive (CAA) with engine oil. The base oil, synthetic engine oil (SEO) SAE 10W40, has blended physically with selected CAA in a volume ratio of 1:0.06. The results show that one of the blended SEO and CAA increases the kinematic viscosity value at temperatures of 40C and 100C. However, the value of the coefficient of friction and wear scar diameter blended between SEO and CAA is higher compared to pure synthetic engine oil. Based on the finding of this study, the role of additional commercial additives can be applied to improve several of the lubricant properties, such as viscosity. It has been demonstrated that synthetic engine oil is superior without additional commercial additives for automotive lubrication.

   

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Title: Crash investigation on frontal vehicle chassis frame using Finite Element Simulation
Author (s):

Nuruddin Ariffin, Kamarul-Azhar Kamarudin, Ahmad Sufian Abdullah and Mohd. Idrus Abd Samad

Abstract:

Car chassis can be considered the primary protective shield for the safety of the passenger during rear-end crashes. This study focuses on the deformation and failure behavior of the frontal car A-pillar chassis frame when subjected to collision with a heavy vehicle. Two different angles of the A-pillar chassis frame used are 45-degree and 70-degree. The crash simulation is conducted by using Finite Element software under the explicit dynamic. The car chassis frame geometries are designed by using SolidWorks 2021 and imported to the finite element software while a rigid block is designed in the finite element software as a rigid body to replicate the heavy vehicle. The chassis body is simulated for two types of materials, Aluminum alloy, and steel. The car speed impacted at 60 km/h. Results show that the intrusion of a rear barrier for 45 degrees of aluminum alloy will stop at 0.03 s but for 70 degrees it will intrude the car frame until the end. For the steel car frame, 45 degrees design is capable to withstand the intrusion of a rear barrier from a serious deform but for 70 degrees the intrusion will continue until the end. Car frame crush behavior, energy dissipation, and vehicle decelerations from the crash simulation were observed.

   

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Title: Development of hybrid nano cutting fluid from tri-agro waste synthesized nanoparticles
Author (s):

Omolayo M. Ikumapayi, Sunday A. Afolalu, Temitayo S. Ogedengbe, Joseph F. Kayode, Abayomi Adegbenjo and Tien-Chien Jen

Abstract:

Despite the vast opportunities that nanotechnology presents due to its application in various sectors, these opportunities are still yet to be maximized in many other areas. Agricultural wastes which ordinarily are a menace to the environment could be synthesized into nanoparticles and used to develop cutting fluids. This study highlights the possibility of the development of such nanofluids from tri-agro wastes. Banana peels, Coconut shells, and Egg shells were synthesized into nanoparticles using the centrifugal process and were characterized using a scanning electron microscope. Nanoparticles were sonicated to ensure homogeneity and mixed with the base fluid to develop the nanofluids. Performance evaluation on grinding of stainless steel plates showed that the developed nanofluids produced a better surface finish of 1.00m than the conventional cutting fluid which produced a surface finish of 1.73 m during the grinding of galvanized steel. Also, results for mild steel showed a better surface finish (0.617 m) when nanofluids were used as against when the conventional cutting fluid was used (1.857 m). Hence, the use of nanofluids developed from tri-agro wastes has not only solved the problem of environmental pollution but has also proved to be a better metal working fluid providing improved surface finish during metal working activities.

   

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Title: Comparative study between a neural network controller and a classic pi applied to an experimental hydraulic system
Author (s):

Jhon Jairo Ramirez Mateus, Francisco Ernesto Moreno Garcia and July Andrea Gomez Camperos

Abstract:

A large part of the industrial processes, when entering competitiveness, must be subject to flexibility so that related aspects can be adapted according to demands at the production level as well as current technological trends. One strategy to appropriate these processes is to adopt the use of control techniques such as Artificial Neural Networks (ANN) inspired by the biological neural networks of the human brain; its advantage is the ability to provide abstract dynamic features from a series of experimental data. Under this concept, an ANN controller system applied to a test hydraulic system was developed, which was compared with a classic PI strategy. Said comparison at the simulation level presented satisfactory results, demonstrating the quality and optimization in the processing, emulation, and control of a physical system with non-linear characteristics. The performance of the networks is noteworthy, the Tau response times for both controllers when the level of the tank decreases are similar, however, the settling time of the neural network was between 20% and 40% faster than the controller PI. The presence of overshoot above 20% was identified by the PI control in response to changes in the setpoint for the size of the tank level.

   

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